RRepoGEO

REPOGEO REPORT · LITE

SkalskiP/awesome-chatgpt-code-interpreter-experiments

Default branch master · commit 84b9adef · scanned 5/16/2026, 8:27:55 PM

GitHub: 1,015 stars · 57 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface SkalskiP/awesome-chatgpt-code-interpreter-experiments, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clarify the repo's nature

    Why:

    CURRENT
    We aim to push ChatGPT + Code Interpreter to its limits, show you what's possible and unlock your creativity! Well, and have a lot of fun doing it! 🔥
    COPY-PASTE FIX
    This repository is a curated collection of awesome experiments and practical use cases for pushing ChatGPT + Code Interpreter to its limits, showing you what's possible and unlocking your creativity! Well, and having a lot of fun doing it! 🔥
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with an appropriate open-source license (e.g., MIT, Apache-2.0) to clarify usage rights.
  • mediumtopics#3
    Enhance repository topics for better categorization

    Why:

    CURRENT
    agent, chatbot, code-interpreter, computer-vision, jailbreak, language
    COPY-PASTE FIX
    agent, chatbot, code-interpreter, computer-vision, jailbreak, language, awesome-list, experiments, use-cases, generative-ai, llm-experiments

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface SkalskiP/awesome-chatgpt-code-interpreter-experiments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ChatGPT with Code Interpreter
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ChatGPT with Code Interpreter · recommended 2×
  2. Google Bard · recommended 1×
  3. jupyterlab/jupyter-ai · recommended 1×
  4. DataCamp Workspace · recommended 1×
  5. Hex · recommended 1×
  • CATEGORY QUERY
    How can I leverage a conversational AI with an integrated Python sandbox for data analysis?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT with Code Interpreter
    2. Google Bard
    3. Jupyter AI (jupyterlab/jupyter-ai)
    4. DataCamp Workspace
    5. Hex
    6. Deepnote
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Pillow (python-pillow/Pillow)
    10. Docker
    11. Google Cloud Run
    12. AWS Lambda

    AI recommended 12 alternatives but never named SkalskiP/awesome-chatgpt-code-interpreter-experiments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What advanced experiments are possible with a large language model capable of running code?
    you: not recommended
    AI recommended (in order):
    1. pytest (pytest-dev/pytest)
    2. JUnit
    3. Git
    4. Flask (pallets/flask)
    5. Django (django/django)
    6. Express.js (expressjs/express)
    7. cProfile
    8. Pandas (pandas-dev/pandas)
    9. NumPy (numpy/numpy)
    10. SciPy (scipy/scipy)
    11. scikit-learn (scikit-learn/scikit-learn)
    12. OpenMM (openmm/openmm)
    13. Pygame (pygame/pygame)
    14. Gymnasium (Farama-Foundation/Gymnasium)
    15. Matplotlib (matplotlib/matplotlib)
    16. Seaborn (mwaskom/seaborn)
    17. Plotly (plotly/plotly.py)
    18. Altair (altair-viz/altair)
    19. ChatGPT with Code Interpreter

    AI recommended 19 alternatives but never named SkalskiP/awesome-chatgpt-code-interpreter-experiments. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of SkalskiP/awesome-chatgpt-code-interpreter-experiments?
    pass
    AI did not name SkalskiP/awesome-chatgpt-code-interpreter-experiments — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts SkalskiP/awesome-chatgpt-code-interpreter-experiments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkalskiP/awesome-chatgpt-code-interpreter-experiments explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo SkalskiP/awesome-chatgpt-code-interpreter-experiments solve, and who is the primary audience?
    pass
    AI did not name SkalskiP/awesome-chatgpt-code-interpreter-experiments — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite